How climate policy commitments influence energy systems and the economies of US states

In the United States, state governments have been the locus of action for addressing climate change. However, the lack of a holistic measure of state climate policy has prevented a comprehensive assessment of state policies’ effectiveness. Here, we assemble information from 25 individual policies to develop an aggregate index of state climate policies from 2000-2020. The climate policy index highlights variation between states which is difficult to assess in single policy studies. Next, we examine the environmental and economic consequences of state climate policy. A standard-deviation increase in climate policy is associated with a 5% reduction in per-capita electricity-sector CO2 emissions and a 2% reduction in economy-wide CO2 emissions per capita. We do not find evidence that more stringent climate policy harms states’ economies. Our results make clear the benefits of state climate policy, while showing that current state efforts are unlikelyto meet the US goal under the Paris Climate Accord.

Year Climate policy stringency Figure S2: Increasing state climate policy commitments over time: Each thin line represents the trajectory of an individual state. The bold line shows the average of our climate policy stringency index across all states, with each state weighted equally. To illustrate change over time, we include labels for a selected group of states whose climate policy stringency spans the distribution in 2020.  Discrimination parameter estimate Figure S3: Discrimination parameters for the policies included in the climate policy index: The figure shows point estimates and 95% intervals defined by the 2.5th and 97.5th quantiles of the posterior distribution for each discrimination parameter estimate. Discriminaton parameters of higher magnitude indicate that the policy is strongly related to our latent climate policy index, whereas discrimination parameters with values close to zero are weakly related to the latent climate policy index. Positive discrimination parameters indicate that the adoption of the policy is associated with an increase in the latent climate policy index, whereas negative discrimination parameters indicate that the adoption of the policy is associated with a decrease in the latent climate policy index.

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Having illustrated the face validity of the climate policy estimates in Figures 1 and 2 from the main text, here we conduct a more systematic validation of the climate policy index. We begin with convergent validation, 1 documenting the strong cross-sectional relationships between our estimates and existing measures of state energy and climate policy. We then turn to construct validation, demonstrating that our climate policy scale is also highly correlated with measures of theoretically related concepts, such as state policy liberalism 2,3 and the ideological preferences of the mass public. 3,4

Convergent Validation
If our estimates provide a valid measure of policy liberalism, they should be strongly related to other (valid) measures of the same concept. We assess the convergent validity of the climate policy index by comparing it with the American Council for an Energy-Efficient Economy (ACEEE) state-level energy efficiency score cards. 5 ACEEE has been ranking states on their efforts to promote energy efficiency each year since 2006. 1 Thus, a strong correlation between our climate policy index and the ACEEE scorecards provides an indication of the convergent validity of our index. Figure S4 shows the correlation between our index and the ACEEE scorecard in 2006, 2010, 2015, and 2020. Across all years, we find that the climate policy index is strongly correlated with state scores on ACEEE's scorecard of state energy efficiency. Moreover, the correlation between our index and ACEEE's scorecard has increased overtime.

Construct Validation
The purpose of construct validation is to demonstrate that a measure conforms to wellestablished hypotheses relating the concept being measured to other concepts. 1 One such hypothesis is that the strength of a state's commitment to promoting a clean-energy transition should be positively correlated with its policy liberalism across the broader state policy agenda. We measure state policy liberalism based on the one-dimensional estimates from Caughey and Warshaw 2 . In Figure S5, we show that states with more liberal policies on the wider state policy agenda have more stringent climate policies.
Another such hypothesis is that the stringency of state climate policies should be correlated with the ideological preferences of the mass public. We measure ideological preferences of the mass public based on the one-dimensional estimates from Tausanovitch and Warshaw 4 . In Figure S6, we show that states with more liberal publics have more stringent climate policies.    Figure 5 in the main paper with and without corrections for measurement error in the climate policy index. 6 All dependent variables are logged, and the climate policy variable is scaled to have a mean of zero and standard deviation of one. Effects are estimated with OLS regression, including state and region-year fixed effects. Standard errors are clustered by state and region-year.

Robustness checks
Here we investigate the sensitivity of our results to the inclusion or exclusion of any of the policies included in our index. First, we estimated a series of climate policy indices, each of which excludes one of the policies included in our full index. The models are estimated in exactly the same way, except that one policy is left out each time. Next, we estimate the effect of climate policy on CO 2 emissions from the electricity sector with each index, as a robustness check to ensure that the results are not overly sensitive to the inclusion or S-10 exclusion of any of the policies. The results are shown in Figure S7 and show that our results are robust to the specific policies included in the model. The point estimates vary slightly, but they are almost all statistically significant (p ≤ 0.05) and similar in magnitude.  We next investigate the robustness of our results to alternative regression specifications. First, we add lagged economic indicators to our main specification, with the CO 2 emissions dependent variables. The results are shown in Figure S8. The direction, magnitude, and precision of the results are consistent with the main results presented in the paper. Second, we run a robustness check in which we add lagged unionization rate to our models assessing the effect of climate policy on economic indicators. These results are shown in Figure S9 below. In this case, some of the coefficients are different in sign, but they remain statistically insignificant. Effect of climate policy on economic indicators Figure S9: Results with controls for lagged unionization rate: The figure shows the results from our main regression specification including state and region-year fixed effects (left panel), and results from regression specifications including state and region-year fixed effects along with one-year lags for unionization rates in each state.Point estimates are shown with 90% (thick lines) and 95% (thin lines) confidence intervals. While the signs on some of the coefficients from these regressions vary from the main results, the pattern of statistical significance is largely consistent with the main results. In all models, n=1,071: 51 states, observed over 21 years.

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6 Policy coding and data sources